Systems, methods and computer program products for offering consumer loans having customized terms for each customer

ABSTRACT

Systems, methods and computer program products take into account the amount, term, and type of consumer loan, as well as data relating to a customer&#39;s credit score, debt burden, and collateral, if any. The invention then calculates an expected probability of default for a loan to that customer, and calculate loan terms that will deliver a minimum return on equity 18%) given the lender&#39;s capital structure and funding rates. These loan terms are then offered to the customer. The customized loan terms include annual percentage rate of the loan, or a yearly fee or loan amount.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to, and thebenefit of U.S. Ser. No. 09/972,785 filed on Oct. 5, 2001 and entitled“SYSTEMS, METHODS AND COMPUTER PROGRAM PRODUCTS FOR OFFERING CONSUMERLOANS HAVING CUSTOMIZED TERMS FOR EACH CUSTOMER.” The '785 applicationclaims priority from U.S. Provisional Patent Application Ser. No.60/238,186, filed on Oct. 5, 2000, titled “Systems, Methods and ComputerProgram Products For Offering Consumer Loans Having Customized Terms ForEach Borrower.” All of which are incorporated herein by reference.

FIELD OF INVENTION

The present invention relates to loans, and more particularly, tosystems, methods and computer program products for offering loans havingcustomized loan terms.

BACKGROUND OF THE INVENTION

Typically, lenders, such as banks, credit card companies and the like,offer loans to consumers based upon a calculation of the rate of returnfor loans given a certain level of risk. Because this calculation is ahighly subjective process, lenders will ensure that loans areappropriately priced for a large pool of applicants to ensure that aminimum return will be met for over all loans. Therefore, the creditprocess begins with a formulation of strategy on how to allocate creditamong customers and products to obtain the highest level of return for agiven level of risk. This is generally a very structured, quantitativeprocess where credit scores are calculated to estimate the expecteddefault rate of a customer based on data from loan applications andcredit bureaus. Credit products are then structured having a limited setof terms and pricing points depending upon an individual customer'scredit score, so that groups of customers having similar credit scoreswill receive the same loan terms. Thus, the conventional wisdom is toprice pools of loans and recoup returns by selling products in largevolume.

A problem with such conventional methods and systems for offering loansis that less creditworthy customers tend to apply in greater numbers.Therefore, credit products must be priced to cover this phenomenon sothat a return is ensured despite the potential of default for a largenumber of customers. Therefore, credit terms are typically priced sothat customers with higher credit scores subsidize the less creditworthycustomers. This typically makes a product less attractive to customershaving higher credit, which amplifies the problem, as less favorablecredit terms are generally unattractive to those with high credit.

What is therefore needed are systems, methods and computer programproducts for determining and setting loan terms for each individualcustomer to cover that customer's risk, so that creditworthy customersare not given unattractive terms to subsidize less creditworthycustomers.

SUMMARY OF THE INVENTION

Systems, methods and computer program products according to the presentinvention take into account the amount, term, and type of customer loan,as well as data relating to a customer's credit score, debt burden, andcollateral, if any. The present invention then calculates an expectedprobability of default for a loan to that customer, and calculatescustom loan terms that will deliver a minimum return on equity (e.g.,18%) given the lender's capital structure and funding rates. These loanterms are then offered to the customer. According to one aspect of theinvention, the custom loan terms include annual percentage rate of theloan. According to another aspect of the invention, the custom loanterms include yearly fee or loan amount.

According to one embodiment of the present invention, there is discloseda method for determining individually customized loan terms for acustomer. The method includes accepting customer credit application datacorresponding to the customer, and accessing credit bureau datacorresponding to the customer, where the credit bureau data contains acredit rating for the customer. The method further includes calculatingan expected probability of default for a loan to the customer based atleast in part upon the customer credit application data and the creditbureau data, and determining customized loan terms that deliver aminimum return on equity for a lender, based at least in part upon ameasurement of likelihood that the customer will default on a loan, thelender's capital structure, and funding rates available to the lender.

According to one aspect of the invention, the method includesrecalculating the customized loan terms for a different term where thecustomer does not accept an offer for the loan. According to anotheraspect of the invention, determining customized loan terms includesdetermining a required return on capital for the lender and calculatinga required return on risk-adjusted assets (RORAA) for the lender.According to yet another aspect of the invention, accessing creditbureau data corresponding to the customer comprises receiving creditbureau data representing the likelihood for the customer to default on aloan.

According to the method, determining customized loan terms that delivera minimum return on equity for a lender can also include determiningcustomized loan terms for an unsecured loan to the customer.Additionally, calculating an expected probability of default for a loanto the customer may be based at least in part upon the customer's riskfrom debt burden. Moreover, determining customized loan terms caninclude determining customized loan terms based at least in part uponoverhead incurred by the lender.

According to another embodiment of the invention, there is disclosed asystem for offering a customer loan terms individually customized forthe customer. The system includes a custom loan manager accessible bythe customer via a computer, and at least one credit bureau incommunication with the custom loan manager. The custom loan managerincludes processing instructions for accepting customer creditapplication data corresponding to the customer, accessing credit bureaudata corresponding to the customer, calculating an expected probabilityof default for a loan to the customer based at least in part upon thecustomer credit application data and the credit bureau data, anddetermining customized loan terms that deliver a minimum return onequity for the lender, based at least in part upon a measurement oflikelihood that the customer will default on a loan, the lender'scapital structure, and funding rates available to the lender.

According to yet another embodiment of the invention, there is discloseda computer program product for use with a data processing system fordetermining customized loan terms for a customer. The computer programproduct includes a computer usable medium having computer-readable codemeans embodied in said medium, the computer-readable code meansincluding computer-readable code means for calculating an expectedprobability of default for a loan to the customer based at least in partupon customer credit application data received from the consumer andcredit bureau data associated with the consumer and received from acredit bureau, and computer-readable code means for determiningcustomized loan terms that deliver a minimum return on equity for alender, based at least in part upon a measurement of likelihood that thecustomer will default on a loan, the lender's capital structure, andfunding rates available to the lender.

A unique aspect of the present invention is that it prices customers asindividuals, rather than as one of a pool of customers, such thatindividual loan terms may be customized for each customer. Thus, thepresent invention offers an individual risk based pricing model thatmakes the loan process more objective by introducing the same level ofrigor to the pricing of loan terms as is in the development of a creditscore. Therefore, instead of offering one lending product, or severalsmall variations, to the mass market, the present invention allows formass customization where each customer's loan is unique in price,amount, and terms to that customer.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 shows a block diagram of a customized loan system, according toone embodiment of the present invention.

FIG. 2 shows a block diagram of the custom loan manager illustrated inFIG. 1, according to one embodiment of the present invention.

FIG. 3 shows a block diagram flow chart illustrating the calculation ofpersonalized loan terms given an expected default rate, according to oneaspect of the present invention.

FIG. 4 shows a block diagram flow chart illustrating the calculation ofpersonalized loan terms for unsecured installment loans and unsecuredlines of credit, according to one aspect of the present invention.

FIG. 5 shows a block diagram flow chart illustrating the calculation ofpersonalized loan terms for loans secured by cash, according to oneaspect of the present invention.

FIG. 6 shows a block diagram flow chart illustrating the calculation ofpersonalized loan terms for loans secured by securities and loanssecured by real property, according to one aspect of the presentinvention.

FIG. 7 shows a block diagram flow chart illustrating the calculation ofrecovery value for loans secured by securities, according to one aspectof the present invention.

FIG. 8 shows a block diagram flow chart illustrating the calculation ofrecovery value for loans secured by real property, according to oneaspect of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likenumbers refer to like elements throughout.

As will be appreciated by one of ordinary skill in the art, the presentinvention may be embodied as a method, a data processing system, or acomputer program product. Accordingly, the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment or an embodiment combining software and hardware aspects.Furthermore, the present invention may take the form of a computerprogram product on a computer-readable storage medium havingcomputer-readable program code means embodied in the storage medium. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices.

The present invention is described below with reference to blockdiagrams and flowchart illustrations of methods, apparatus (i.e.,systems) and computer program products according to an embodiment of theinvention. It will be understood that each block of the block diagramsand the flowchart illustrations, and combinations of blocks in the blockdiagrams and combinations of the blocks in the flowchart illustrations,can be implemented by computer program instructions. These computerprogram instructions may be loaded onto a general purpose computer,special purpose computer, or other programmable data processingapparatus to produce a machine, such that the instructions which executeon the computer or other programmable data processing apparatus createmeans for implementing the functions specified in the block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theblock or blocks of the flowchart, or clock or blocks of the diagrams.

Accordingly, blocks of the block diagrams and the flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functionsand program instruction means for performing the specified functions. Itwill also be understood that each block of the block diagrams and theflowchart illustrations, and combinations of the respective blocks, canbe implemented by special purpose hardware-based computer systems whichperform the specified functions or steps, or combinations of specialpurpose hardware and computer instructions.

According to one embodiment of the invention systems, methods andcomputer program products of the present invention can be utilized tooffer customized loans for a number of loans, including both unsecuredand secured loans. For instance, the present invention may be used withunsecured installment loans, unsecured lines of credit, loans secured bycash, loans secured by securities (e.g., equity, bonds, mutual funds,etc.), and loans secured by real property (e.g., home mortgages).However, it will be appreciated by one of ordinary skill in the art thatthe present invention may also be extended to additional types of loansother than those listed herein, such as for auto loans, charge cards,and revolving credit cards. Therefore, the examples used herein areintended as illustrative embodiments of the present invention, and arenot intended to be limiting as to the scope of the present invention.

FIG. 1 shows a block diagram of a customized loan system 5 according toone embodiment of the present invention. The customized loan system 5includes a lender 10, a customer 15, a custom loan manager 20, and athird party information provider 25. The system also includes one ormore communication medium(s) 30 through which each of the components 10,15, 20, 25 can communicate. The communication medium(s) 30 isrepresentative of any network or networks through which customers andlenders communicate for the purchase and sale of loan products,including conventional telephone networks, computer networks, or theInternet. Although the individual components 10, 15, 20, 25 areillustrated as separate components communicating with the aid of thecommunication medium(s) 30, it should be appreciated that one or more ofthe components 10, 15, 20, 25 can reside on the same network. Forinstance, the custom loan manager 20 may be located on the same networkas, and local to, the lender 10, such that the communication mediumbetween the two components is a local area network (LAN). Additionally,according to another illustrative example, a customer 15 may be incommunication with a lender 10 via an Internet connection where thelender 10 offers loans via a web page accessed by the customer throughan Internet connection, such as provided by an Internet Service Provider(ISP). As will be appreciated by those of skill in the art, the modes ofcommunication between the entities of the system 5 of FIG. 1 may beaccomplished by any well known communication means, and are not limitedto any particular means stated herein. Furthermore, although the presentinvention will be described herein relative to components incommunication via the Internet, its application is not so limited and isintended to be used on any distributed system in which customers andlenders interact for the purpose of providing and obtaining loans.

According to one aspect of the invention, the custom loan manager 20 isaccessible via the Internet and includes computer hardware and/orsoftware for implementing the methods described herein, including themethods for producing customized loan terms for customers. FIG. 2 showsa block diagram of the custom loan manager 20 of FIG. 1, according toone embodiment of the present invention. The custom loan manager 20generally includes a processor 35, bus 40, display device/input device45, memory 50, storage device 65, and network interface 70. Theprocessor 35 communicates with other elements within the custom loanmanager 20 via a system interface or bus 40, and is responsible, alongwith an operating system 60 residing within the memory 50, for managingthe functions of the custom loan manager 20. The display device/inputdevice 45, for example, a keyboard or pointing device in combinationwith a monitor, receives and outputs, via a display, data produced orprocessed by the custom loan manager 20. For instance, as will beexplained in detail with reference to FIGS. 3-8, the custom loan manager20 produces customized loan terms that may be displayed to an operatorof the custom loan manager 20 via the display device/input device 45.The display device can include a monitor, printer, personal digitalassistant, or other well known device for displaying data to anoperator. In addition to a display device/input-device 45, the customloan manager 20 includes a network interface 70 for interfacing andcommunicating with other network devices. Therefore, customized loanterms generated by the custom loan manager 20 may be transmitted toother network components via the network interface 70 in addition tobeing displayed on a display device/input device 45. Furthermore, thenetwork interface 70 enables the custom loan manager 20 to receive datafrom third parties 25, such as credit bureaus, and from other systemslocal to the custom loan manager 20. As explained in detail below, thisdata can be used by the custom loan manager 20 to generate customizedloan terms for customers.

The memory 50 located within the custom loan manager 20 includes a loanprocessor module 55, which controls the operation of the custom loanmanager 20 with the assistance of the processor 35 and the operatingsystem 60. The operating system 60 enables execution of the loanprocessor module 55 by the processor 35. The custom loan manager 20 alsoincludes a storage device 65, such as a hard disk drive, which containsfiles that are utilized by the loan processor module 55 in calculatingpersonalized or customized loan terms. The storage device 65 may containone or more tables or databases that store customer credit information,such as historical credit information received from credit bureaus,customers, and third parties. The storage device 65 can also storepersonal data associated with a customer, such as the customer's homeaddress, social security number, home telephone number, and the like,and may also contain data associated with a particular loan or creditaccount maintained by the customer. It will be appreciated by one ofordinary skill in the art that one or more of the custom loan manager 20components may be located geographically remotely from other custom loanmanager 20 components. Furthermore, one or more of the components may becombined, and additional components performing functions describedherein may be included in the custom loan manager 20.

FIGS. 3-8 illustrate the methods implemented by the custom loan manager20, and more specifically, methods implemented by the loan processormodule 55 with the aid of the processor 35 and operating system 60, ingenerating personalized loan terms for customers. The present inventionallows a lender to offer mass customization of loans to customers, suchthat each customers loan may be unique in price, amount, and/or terms tothat customer. The present invention also considers the volatility ofthe estimates in its calculation and links customers' price to theirrisk, which allows a lender to either tighten or loosen credit termsversus the market rather than other benchmarks, such as internalbenchmarks. Furthermore, the present invention reduces the subsidiespaid by better customers (i.e., customers that pay), which can enhance alender's market position in the competitive credit market.

Generally, the present invention takes five components intoconsideration in calculating customized loan terms regardless of thetype or category of loan. These components are (1) cost of funds, (2)expected operating expenses, (3) expected credit losses, (4) ‘risk’ orunexpected credit loses, and (5) excess returns due to activemanagement. The cost of funds, expected operating expenses and expectedcredit loses are calculated based upon a detailed analysis of aborrower/lender such that these terms are known at the time a customizedloan is determined. It should be appreciated that, as opposed to thecustomer, one example of a borrower/lender is a credit card companywhich takes a loan from a lender in order to offer credit service. Justas the customer must pay the borrower/lender, the borrower/lender mustrepay the lender of the funds which allow the credit card company toperform its services. Given these known terms, it is credit (loan)application data and credit bureau data that are utilized by the customloan manager 20 to determine the final two values, as will be explainedin detail below. The premium for unexpected credit loses measures therequired return on equity capital assigned to a transaction, and excessreturns from management is the excess spread over the minimum price thatmanagement can extract from inefficient pricing in the market.

Regardless of the type of loan that is customized, systems, methods andcomputer program products of the present invention include commoncalculations and steps required for every type of customizable loan. Onesuch calculation is that which is illustrated in FIG. 3. FIG. 3 shows ablock diagram flow chart 70 illustrating the calculation of personalizedloan terms given an expected default rate, according to one aspect ofthe present invention. As stated above, these steps may be performedusing computer hardware and/or software, such that each of the steps maybe executed on a computer. Likewise, steps requiring the input of databy a customer or third party can be implemented over a computer network,such as a local area network (LAN) or a wide area network such as theInternet. For instance, customers seeking customizable loans may accessthe present invention, which can operate as computer software residenton a server accessible via the Internet, so that customers in diversegeographical locations can take advantage of the present invention.Therefore, it will be appreciated that the invention may be implementedthrough the use of one or more graphical user interfaces (GUI) on one ormore web pages, wherein the GUIs facilitate the input or transfer ofinformation to systems, methods and computer program products of thepresent invention, as is well known in the art. Furthermore, it will beappreciated that where the present invention is implemented over anetwork the invention may collect data or information from one or morethird parties, such as credit bureaus, in a format that is usable by thepresent invention, or may be converted by well known means to a formatuseable by the present invention.

Referring now to FIG. 3, the calculation of a customized interest rateor annual percentage rate (APR) for a loan given an expected defaultrate is illustrated. This calculation is made regardless of the type ofloan offered to the customer, and is the basis for determining acustomized APR for a given customer. As illustrated in FIG. 3, the costand overhead associated with the funds and loan is initially determined.The cost of funds is the cost incurred by a lender in loaning money to aborrower. For instance, a credit card company incurs costs associatedwith payment of goods for a customer because the credit card companyborrows money, paying interest on the amount borrowed. Therefore, thecost associated with the borrowing of money is the cost of fund in thisillustrative example. The cost of funds for one or more transactions(loan calculations) may be input by an operator of the custom loanmanager 20, but is preferably automatically accessed by the loanprocessor module 55 from the storage device 65 or one or more networkelements accessible via the network interface. Because the determinationof the cost of funds is well known to those of skill in the art, thecost of funds will not be further discussed herein. The overheadassociated with a loan is also determined and accessed in a similarmanner, and is well known to those of skill in the art. This overhead isthe amount that it costs a lender to process the loan, including costsassociated with billing for the loan, sending statements to thecustomer, advertising, and the like. After the cost of funds andoverhead are determined (block 70), they are both stored in the memory50 of the custom loan manager 20 for use in subsequent calculationsexplained in detail below.

Next, the required return on risk adjusted assets (RORAA) is determinedby the loan processor module 55 (block 80). Calculating the RORAA isalso well known to those of ordinary skill in the art. However, thefollowing calculations and tables represent an illustrative example of aRORAA calculation given benchmark numbers for a required return onequity (18%), a required return on capital (12.21%), and a 34% tax rate.It should be appreciated that the first step is calculating a minimumprofit that is acceptable while at the same time covering the cost ofthe equity capital assigned to a transaction. The RORAA calculation isbased on the capital requirements listed in the Basle accords, and isadvantageous for pricing in markets or products where there isinsufficient data to calculate the risk based capital requirement.

The following example calculates the absolute minimum spread a typicalbank should be willing to accept on a “risk free” loan, based onshareholder return requirements and regulatory capital constraints. Itprovides a consistent floor for all loan-pricing decisions that will notvary over the credit cycle. This example includes several assumptions,such as: zero losses, maximum leverage (capital equal to 8% of riskadjusted assets, common equity constituting all of Tier 1 capital, andfurther leveraged through full use of Tier 2 capital), capital isinvested in US Treasury securities and is not allocated to individualtransactions, and no overhead expenses are assumed.

Table 1 shows an illustrative example of risk weighting, which may bedefined by an operator of the loan manager and stored within the storagedevice 65 for use in computing the RORAA:

TABLE 1 Illustrative Example of Risk Weighting Based on Category ofTransactions Risk Weighting Category  0% Cash Claims on centralgovernments 10% Claims on domestic public sector entities 20% Generalobligations of state and local govt. Claims on domestic depositaryinstitutions 50% Revolving credit underwriting agreements Revenue bondsof state and local governments Residential mortgages 100%  CommercialLoans Consumer Loans Standby letters of credit (for credit enhancement)

Based on the risk weighting, a risk adjusted balance sheet is created.

TABLE 2 Risk-Based Balance Sheet Risk Actual Reported Weighting AdjustedAssets Investments 7 7  0% 0 Net Loans 90 80 100%  90 Revolving Loans 2050% 10 Total Assets 87 100 Capital/Assets Ratio 9.20% 8.00% Liabilities& Equity Funding 80 Subordinated Debt 1 n.b. loan loss reserve = 1Preferred Stock 2 Common Equity 4 Total 87

Next, the after-tax cost of capital is calculated. This calculation isillustrated in Table 3.

TABLE 3 Calculating Cost of Capital Cost of Capital Calculations (2) (1)After tax Cost (1)*(2) Amount pre-tax Cost (34% Tax Rate) Net Tier 1Common Equity 4 0.18 0.1800 0.7200 Tier 2 Loan Loss reserve 1 — — —Subordinated Debt 1 0.09 0.0594 0.0594 Preferred Stock 2 0.1 0.10000.2000 Total 8 0.9794 Weighted Average Cost of Capital = .979/8 = 12.24%(after-tax)

Using this required return on capital and assuming an 8.5% yield oninvestments, the required return on risk-adjusted assets (RORAA) may becalculated. This calculation is illustrated in Table 4.

TABLE 4 Calculating RORAA Required Return on Risk Adjusted Assets(RQRAA) Required Return Calculation WACC 12.24% *Capital 8 0.9784Pre-tax Yield 0.085 *(1 -tax Rate) 0.66 *Investments 7 0.3927 TotalProfit Required 0.9794 (Earnings on 0.3927 Investments) RORAA 0.5867

Since risk-adjusted assets equal 100, the required return is 0.5865 onrisk-adjusted assets. Specific pricing minimums can now be establishedfor different classes of loans.

EXAMPLE 1 Pricing a Loan

${RORAA} = \frac{( {{Pre}\text{-}{tax}\mspace{14mu} {return}} )*( {1 - {{tax}\mspace{14mu} {rate}}} )}{( {{nominal}\mspace{14mu} {amounts}} )*( {{risk}\mspace{14mu} {weighting}} )}$$\begin{matrix}{{{Pre}\text{-}{tax}\mspace{14mu} {return}} = \frac{({RORAA})*( {{nominal}\mspace{14mu} {amount}} )*( {{risk}\mspace{14mu} {weighting}} )}{( {1 - {{tax}\text{-}{rate}}} )}} \\{= \frac{(0.5865)*(1.00)*(1.00)}{( {1 - 0.34} )}} \\{= {0.889\mspace{14mu} {or}\mspace{14mu} 89\mspace{14mu} {basis}\mspace{14mu} {{points}.}}}\end{matrix}$

EXAMPLE 2 Pricing a Line of Credit

$\begin{matrix}{{{Pre}\text{-}{tax}\mspace{14mu} {return}} = \frac{({.5865})*(1.00)*({.50})}{( {1 - 0.34} )}} \\{= {0.444\mspace{14mu} {or}\mspace{14mu} 44\mspace{14mu} {basis}\mspace{14mu} {{points}.}}}\end{matrix}$

This illustrative example indicates that an absolute minimum of 89 basispoints must be earned on loan outstandings and 44 basis points onrevolving lines of credit for earnings objectives to be achieved. Thepricing will go up if credit risk and operating expense are included.According to one aspect of the invention, in markets having sufficientinformation to calculate the volatility of the default rate a risk basedcapital adjustment can be used, wherein capital is assigned to thetransaction based on the volatility of the default rate and tolerancefor risk.

Referring again to FIG. 3, once the RORAA and required pre-tax returnhas been calculated by the custom loan manager 20 using the abovemethod, or input into the loan manager 20 by an operator, the requiredpre-tax return is combined with the cost of funds and overhead (block85) to determine the risk free rate. Therefore, the Risk Free Rateequals the Cost of Funds plus the Required Return plus the Overhead.Continuing with the hypothetical pre-tax return computed from the RORAAabove, and using hypothetical terms for cost of funds and overhead, theRisk Free Rate may be illustrated as follows:

$\begin{matrix}{{{Risk}\mspace{14mu} {Free}\mspace{14mu} {Rate}} = {{Funding} + {{Required}\mspace{14mu} {Return}} + {Overhead}}} \\{= {{7.5\%} + 0.889 + {2.344\%}}} \\{= {10.73\%}}\end{matrix}$

Finally, given a default rate the required interest rate for acustomized loan to return minimum required return on equity can becalculated. In the calculations immediately below the default rate isassumed to be 5%, such that there is assumed a 5% probability that acustomer will default on a loan. The computation of the default rate isdiscussed in great detail with respect to FIGS. 4-8, which describevarious computations performed by the loan processing module 55 todetermine the default rates, including computations that include the useof credit bureau data, such as credit ratings, in determining defaultrates. The following formula is used to determine the customized APR fora one year loan:

${{Interest}\mspace{14mu} {rate}} = \frac{( {1 + {RFR}} ) - ( {( {{Default}\mspace{14mu} {rate}} )*( {{Recovery}\mspace{14mu} {Rate}} )} ) - 1}{( {1 - {{Default}\mspace{14mu} {Rate}}} )}$

Assuming a 5% default rate, the following formula is used obtain acustomized APR where the recovery rate is 0.2:

$\begin{matrix}{{{Interest}\mspace{14mu} {Rate}} = \frac{( {( {1 + {.1073}} ) - ( {{.05}*{.2}} )} ) - 1}{.95}} \\{= {15.39\%}}\end{matrix}$

This calculation is performed by the loan processor module 55, andsolves for an interest rate that produces the same total return, afteradjusting for defaults and recoveries, as the return required on arisk-free investment. The credit spread over the risk free rate is theadditional yield required to compensate for default risk. Using thecomputations above the custom loan manager 20 can offer personalizedinterest rates to customer given a default rate associated with thecustomer.

FIG. 4 shows a block diagram flow chart illustrating the calculation ofpersonalized loan terms for unsecured installment loans and unsecuredlines of credit, according to one aspect of the present invention. Asshown in FIG. 4, a customer must first complete a credit application, asare known in the art. Credit applications typically include fields forthe purposes of identifying the customer (e.g., social security number,name, address, etc.) the customer's accounts, the customer's income, andlike data for the purposes of determining credit worthiness. Thisinformation is received (block 100) by the custom loan manager 20 of thepresent invention, either automatically or through a manual process. Forinstance, the credit application may be an electronic applicationlocated at one or more websites, and the credit data transferred to thesystem of the present invention. Next, the present invention pulls oraccesses credit data from one or more third parties, such as creditbureaus (e.g., Equifax, Experian, and TransUnion), as are well known inthe art (block 105). This information preferably includes a customer'scredit score and a default rating that indicates the probability thatthe customer will default on a loan. Additionally, this data can includedata related to the customer's identity, credit checks run on thecustomer by potential lenders, public record information, collectiondata, and the like. Additionally, any information about the customerstored within the custom loan manager about the customer may also beaccessed (block 105). After credit application data, third party data(e.g., credit bureau data) and stored customer information data isaccessed, the information is preferably temporarily stored in memory 50.Alternatively, the information may be stored in the storage device 65.From the credit application and credit bureau data an expected defaultrate for the customer may be calculated, as is well known in the art.According to one aspect of the invention, the expected default rate canbe based entirely upon credit bureau data, which may indicate aprobability of default for a loan to a customer based on credit ratinginformation and national averages or historical and/or projectedstatistics for similarly situated customers. According to another aspectof the invention, the expected default rate may be based in part uponcredit bureau data and credit application data, such as the customer'sincome. Additionally, separate default rates may be taken based uponinformation received from one or more credit bureaus, and combined withdefault rate information based on credit application data or receivedfrom third parties. Moreover, different default rates for a customer maybe weighted and combined to generate an accurate expected default rate,or a worst-case scenario default rate may be determined. It ispreferable that the loan processor module 55 calculate an expecteddefault rate based at least upon credit bureau data and customer creditapplication data, where the loan processor module computes an weightedaverage based upon data typically utilized in the computation of defaultrates. According to one aspect of the invention, the default rateinformation can be combined based upon Bayes Theorem, which results in acombined default rate.

In addition to calculating an expected default rate (block 110), thecustom loan manager 20 also calculates the risk from the customer due tothe customer's debt burden. This is another measurement of thelikelihood that the customer will default on a loan. To perform thiscalculation, objective data, such as the customer's income, is used inconjunction with the Blacksholes Model for computing default risk, whichis well known in the art. For instance, it may be assumed that a loanwill default if the amount owed by the customer is larger than thecustomer's income. An illustrative computation of the probability ofdefault from debt burden is illustrated in Table 5:

TABLE 5 Computing Probability of Default From Debt Burden Theprobability of default from debt burden Amount of Loan 20 RFR 10.73%Income 95 Default rate  5% Recovery rate  20% Risk 1.53 Fexp(−rT) = =17.97 a1 = (−ln(V0/F) − (r − s{circumflex over ( )}2/2)T)/s@sqrt(t) =−1.6500 a2 = a1 − s*sqrt(T) = −3.1800 N(a1) = 0.0495 N(a2) = 0.0007 P0 =Fexp(−rT)N(a1) − V0N(a2) = 0.8188 B0 − Fexp(−rT) − P0 = 17.15 InterestRate = ln(Amount/B0) = 15.40% Spread= d₁ = 4.67%

Because it is assumed that a loan will default if the amount owed islarger than income, the risk from debt burden is a function of the debtburden and the volatility of a customer's income. The debt burden isknown, while the volatility is calculated using government statistics onincome movements. Table 6 shows an illustrative income movement matrix:

TABLE 6 Income Matrices Transition Matrix of Income Quintiles 1 2 3 4 51 90.6% 5.2% 2.4% 1.4% 0.4% 100.0% 2 5.0% 87.0% 4.4% 2.4% 1.2% 100.0% 32.2% 4.2% 87.0% 4.4% 2.2% 100.0% 4 1.4% 2.4% 4.0% 87.6% 4.6% 100.0% 50.8% 1.2% 2.2% 4.2% 91.6% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Transition Matrix Translated into Cumulative Annual Volatility 1 2 3 4 56 7 1 25.10% 29.36% 32.19% 34.17% 35.57% 36.59% 37.31% 2 6.81% 8.91%10.68% 12.18% 13.47% 14.56% 15.51% 3 2.45% 3.22% 3.88% 4.45% 4.93% 5.35%5.70% 4 0.33% 0.34% 0.30% 0.26% 0.28% 0.38% 0.54% 5 3.91% 5.40% 6.82%8.17% 9.45% 10.67% 11.82%

Given these income volatility figures for any given debt burden, theloan processor module 55 computes the probability of default from debtburden using Blacksholes Model.

Referring again to the flowchart of FIG. 4, after the risk from debtburden is calculated (block 115) in the manner specified above, therequired return under regulatory capital is computed (block 125). Asdescribed in detail above, the required return under regulatory capitalis required, in addition to funding costs and overhead, to determine therisk free rate, which in turn is required to determine a customized loanterm that delivers a minimum return on equity for a lender based in partupon the likelihood that the customer will default on the loan. Wherethere is a developed market, the volatility of the default rate may becalculated and a risk based capital adjustment can be determined (block135), where capital is assigned to the transaction based on thevolatility of the default rate and tolerance for risk. In markets thatare not developed, a final price is calculated (block 140) using themethod indicated at block 90 of FIG. 3.

As discussed above, the calculation by the loan processing module 55 ofthe expected default rate (block 110) and/or risk from debt burden(block 115) may be used as the default rate in determining the interestrate for a customer's customized loan. According to one aspect of theinvention, the loan processing module 55 calculates an interest ratebase solely on the expected default rate. According to another aspect ofthe invention, the loan processing module calculates an interest ratebased entirely upon the risk from debt-burden. According to yet anotheraspect of the invention the loan processing module calculates aninterest rate based upon the default rate provided by the credit bureau.However, because each of these taken alone is less reliable than thecombination of the default measurements, it is preferred that at leasttwo of the techniques are combined by the loan processor module 55 usinga weighted average technique such as Bayes Theorem. Referring again toFIG. 4, the calculation of a combined, adjusted or averaged default rateis illustrated as optionally occurring at block 120 to indicate that theloan processing module may optionally be configured to adjust thedefault rate such that it is based upon more than one default ratedetermination.

FIG. 5 shows a block diagram flow chart 145 illustrating the calculationof personalized loan terms for loans secured by cash, according to oneaspect of the present invention. As in the process illustrated in FIG.4, a credit application is received from a customer (block 150), andthird party information or data is collected or accessed, as is locallystored customer information or data (block 160). Similarly, as in block110 of FIG. 4, an expected default rate and the risk from debt burdenare each calculated (blocks 160, 165) by the loan processor module 55.Next, the loan processor module 55 computes an expected recovery on aloan default (block 170) considering expenses for collecting on thedefault. This value is calculated as 90% of the cash deposit. However,it should be appreciated that this value is arbitrary and can be varied.Next, as in the process discussed with respect to FIG. 4, an adjusteddefault rate may be calculated. According to one preferred aspect of theinvention, where a loan is secured by cash the adjusted default rate iscalculated as a weighted average of the debt burden, credit bureausupplied default probability or score and recovery rate (block 175).

As in FIG. 4, after the adjusted default rate is calculated (block 175),the required return under regulatory capital is computed (block 180). Asdescribed in detail above, the required return under regulatory capitalis required, in addition to funding costs and overhead, to determine therisk free rate, which in turn is required to determine a customized loanterm that delivers a minimum return on equity for a lender based in partupon the likelihood that the customer will default on the loan. Wherethere is a developed market, the volatility of the default rate may becalculated and a risk based capital adjustment can be determined (block190), where capital is assigned to the transaction based on thevolatility of the default rate and tolerance for risk. Regardless ofwhether a capital adjustment is used, a final price (e.g., interestrate) is calculated (block 190) using the method indicated at block 90of FIG. 3 and offered to the customer.

FIG. 6 shows a block diagram flow chart 200 illustrating the calculationof personalized loan terms for loans secured by securities and loanssecured by real property, according to one aspect of the presentinvention. It will be appreciated that the process performed by thesystem of the present invention in calculating customized loan terms forloans secured by securities and loans secured by real property islargely identical to the process for calculating customized loan termsfor loans secured by cash, but for the step of calculating a recoveryvalue, which is more speculative than recovering a percentage of a cashdeposit. Therefore, like the process of FIG. 5, a credit application isreceived from a customer (block 205), and third party information andstored customer information is accessed (block 210). After an expecteddefault rate (block 215) and risk from debt burden is calculated (block220), the recover value is calculated (block 225). Calculation ofrecovery value is discussed in detail with reference to FIGS. 7 and 8.After the recovery value is calculated, an adjusted default rate iscalculated (block 230) in the same manner as in block 175 of FIG. 5.Thereafter the required return under regulatory capital is calculated(block 235), and a final price is calculated (block 250), which may takein consideration volatility of the default rate by calculating RORACusing the required return (block 245).

FIG. 7 shows a block diagram flow chart 255 illustrating the calculationof recovery value for loans secured by securities, according to oneaspect of the present invention. The purpose of the calculation ofrecovery value is to value the loan collateral for purposes of pricingloan terms. This is performed by calculating the potential drawdown of aportfolio using the variance of the asset price and the correlationamong the assets. In this process the first step is inputting theportfolio composition (block 260). Thereafter the volatility of returnsfor each asset is calculated as is its correlation with the other assets(blocks 265 and 270). These figures are then used to estimate the valueas collateral of each individual customer's portfolio. An illustrativeexample of this process is shown in Table 7 below, with the assetscomprising stocks ‘MSFT’, ‘LMS’ and ‘AJG’:

TABLE 7 Historical Volatility of Stock Assets Weight Volatility AssetMSFT 33.33% 152.57% LMS 33.33% 38.69% AJG 33.33% 49.88% Total  100%Correlation 1, 2 −20.65%  1, 3 89.05% 2, 3 −25.18%  Value of Portfolio10000 Confidence Level NA Volatility Matrix 1.52565496 0 0 0 0.386880890 0 0 0.49881863

Given these historical volatility figures, which are calculated by theloan processor module 55 according to well known methods, the expectedvolatility of the portfolio can be computed. A confidence level for eachcategory of transaction is set as a multiple of the volatility. Forexample if the loan processor 55 is set to have 95% confidence that thecollateral calculation is correct, the Volatility is multiplied by1.645.

Confidence Level 95% Number of STD's 1.644853 Volatility Matrix MSFT LMSAJG Correlation Matrix MSFT 250.95% 0.00% 0.00% 1 −0.20649 0.89047 LMS0.00% 63.64% 0.00% −0.20649 1 −0.25184 AJG 0.00% 0.00% 82.05% 0.89047−0.25184 1 VC Matrix 2.50947814 −0.5181779 2.23462695 −0.13140140.6363622 −0.160264 0.73061969 −0.2066338 0.82048332 VCV Matrix6.29748053 −0.3297489 1.83347413 −0.3297489 0.40495685 −0.1314941.83347413 −0.131494 0.67319287 VAR Matrix Weighting Matrix 33.33%33.33% 33.33% WVCV Matrix 260.04% −1.88% 79.17% WVCVW 112.45%Diversified Volatility 106.04% Undiversified Volatility 132.21%

Thus, the volatility of this portfolio as collateral is 106%. The Mertonmodel and Blacksholes Model described above with respect to FIG. 4 arethen used by the loan processor module 55, with reference to tablesstored within the storage device 65, to calculate the expected recoveryfor each level of coverage (block 270). The recovery rates andassociated risks (or default rates) for each level of coverage areillustrated in Table 8 for the above example. The default rates in thetable are used by the loan processor module 55 in computing the interestrates for each loan having a specified asset portfolio.

TABLE 8 Recovery Rates and Risks for Portfolio Margin 10.00% 20.00%30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Term 1 1 1 1 11 1 1 1 1 Variance 106.0% 106.0% 106.0% 106.0% 106.0% 106.0% 106.0%106.0% 106.0% 106.0% N(1) −1.64122 −0.98756 −0.60519 −0.3339 −0.123460.04847 0.19384 0.31977 0.43084 0.5302 N(2) −2.70163 −2.04796 −1.66559−1.3943 −1.18387 −1.01193 −0.86656 −0.74063 −0.62956 −0.5302 Risk 0.345%2.028% 4.790% 8.161% 11.823% 15.579% 19.309% 22.946% 26.449% 29.799%Recovery 99.655% 97.972% 95.210% 91.839% 88.177% 84.421% 80.691% 77.054%73.551% 70.201% Rate

FIG. 8 shows a block diagram flow chart 280 illustrating the calculationof recovery value for loans secured by real property, according to oneaspect of the present invention. For mortgages, the same generalapproach is utilized as was outlined with reference to FIG. 7, but forthe inclusion of carrying costs and repossession expenses in thecalculation. These costs and expenses must be retrieved by the loanprocessor (block 300) module 55 from databases within the storage device65, or from third parties via the network interface 70. Using tables forhome values, the expected recovery rate for different loan to value(LTV) ratios (blocks 285, 295, 305) can be calculated by the custom loanmanager 20 using the methods described in FIG. 7, above. This provides ameasure of risk, or a default rate, for different loan to value ratios,which allows for the calculation of interest rates, as detailed withreference to FIG. 4. For instance, the loan processor module 55calculates the expected recovery rate in the following example, whichillustrates recovery rates for different loan to value rations for ahome with a value of £100,000 located in the London suburbs (with ahistorical volatility of 7.1%):

TABLE 9 Risk and Recovery Rates For Real Property Loan Amount 30,00040,000 50,000 60,000 70,000 80,000 House Value 100,000 100,000 100,000100,000 100,000 100,000 LTV 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%Term 30 30 30 30 30 30 Req Return 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%COF 0 0 0 0 0 0 Variance 7.1% 7.1% 7.1% 7.1% 7.1% 7.1% Variance{circumflex over ( )}2 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% Fexp(−rT) 30000.0040000.00 50000.00 60000.00 70000.00 80000.00 A1 −2.901535 −2.16177−1.587964 −1.11913 −0.722736 −0.379365 A2 −3.290418 −2.550653 −1.976847−1.508013 −1.11162 −0.768248 Risk 0.050% 0.538% 2.403% 6.578% 13.315%22.117% Recovery Rate 99.950% 99.462% 97.597% 93.422% 86.685% 77.883%

Each of the block diagram flow charts illustrate an offer made to acustomer from the custom loan manager after the calculation of acustomized loan based upon the probability of default, which in thesecured loan scenarios discussed above is directly related to the valueof collateral and the cost for recovering value for that collateral.However, if the customer does not wish to accept a loan term provided bythe custom loan manager 20 to the customer, such as via a web page, thecustomer can request that the loan terms be recalculated by the customloan manager 20. Thereafter the custom loan manager 20 can change one ormore values, such as the loan term. Additionally, the custom loanmanager 20 will allow the customer to input a yearly fee (as collateral)or change the value of the loan, which may result in the calculation ofa lower default rate, less overhead, or other terms that may result inadvantageous pricing of the interest rate. Preferably the custom loanmanager 20 allows a customer to enter multiple scenarios (e.g.,different loan values, yearly fees, etc.) so that the customer canobtain beneficial loan terms.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for purposes of limitation.

1. A method comprising: calculating, by the computer-based system fordetermining loan terms, a weighted expected probability of default for aloan to a customer; calculating, by the computer-based system, a riskfrom debt burden of the customer based at least in part upon avolatility of income; calculating, by the computer-based system, a riskfree rate; and determining, by the computer-based system, loan termsthat are above the risk free rate, wherein said loan terms are based atleast in part upon the weighted expected probability of default, acapital structure of a lender, and funding rates available to thelender.
 2. The method of claim 1, wherein the determining of loan termsincludes determining a required return on capital for the lender andcalculating a required return on risk-adjusted assets (RORAA) for thelender.
 3. The method of claim 1, wherein the determining of loan termsincludes determining loan terms for an unsecured loan to the customer,4. The method of claim 1, wherein calculating a weighted expectedprobability is based at least in part upon the risk from debt burden ofthe customer.
 5. The method of claim 1, wherein the determining of loanterms includes determining loan terms based at least in part upon anoverhead incurred by the lender.
 6. The method of claim 1, wherein thevolatility of income is determined from aggregate income movements of apopulation.
 7. The method of claim 1, wherein said loan terms deliver apredetermined minimum return on equity for a lender.
 8. The method ofclaim 1, weighted the expected probability of default for the loan isbased at least in part upon weighting a first expected probability ofdefault and a second expected probability of default, wherein each thefirst expected probability and the second expected probability are basedon customer credit application data and credit bureau data.
 9. Themethod of claim 9, wherein the credit bureau data contains a creditrating for the customer, and wherein each the first expected probabilityand the second expected probability are based on the credit rating. 10.The method of claim 9, further comprising accepting, by thecomputer-based system, the customer credit application datacorresponding to the customer.
 11. The method of claim 9, furthercomprising accessing, by the computer-based system, the credit bureaudata corresponding to the customer.
 12. The method of claim 11, whereinthe accessing of credit bureau data includes receiving credit bureaudata representing the likelihood for the customer to default on a loan.13. A system comprising: a processor for determining loan terms, atangible, non-transitory memory communicating with the processor, thetangible, non-transitory memory having instructions stored thereon that,in response to execution by the processor, cause the processor toperform operations comprising: calculating, by the processor, a weightedexpected probability of default for a loan to a customer; calculating,by the processor, a risk from debt burden of the customer based at leastin part upon a volatility of income; calculating, by the processor, arisk free rate; and determining, by the processor, loan terms that areabove the risk free rate, wherein said loan terms are based at least inpart upon the weighted expected probability of default, a capitalstructure of a lender, and funding rates available to the lender. 14.The system of claim 13, wherein the determining of loan terms includesdetermining a required return on capital for the lender and calculatinga required return on risk-adjusted assets (RORAA) for the lender. 15.The system of claim 13, wherein the determining of loan terms includesdetermining loan terms for an unsecured loan to the customer.
 16. Thesystem of claim 13, wherein calculating a weighted expected probabilityis based at least in part upon the risk from debt burden of thecustomer.
 17. The system of claim 13, wherein the determining of loanterms includes determining loan terms based at least in part upon anoverhead incurred by the lender.
 18. The system of claim 13, wherein thevolatility of income is determined from aggregate income movements of apopulation,
 19. The system of claim 13, wherein said loan terms delivera predetermined minimum return on equity for a lender.
 20. An article ofmanufacture including a non-transitory, tangible computer readablestorage medium having instructions stored thereon that, in response toexecution by a computer-based system for determining loan terms, causethe computer-based system to perform operations comprising; calculating,by the computer-based system, a weighted expected probability of defaultfor a loan to a customer; calculating, by the computer-based system, arisk from debt burden of the customer based at least in part upon avolatility of income; calculating, by the computer-based system, a riskfree rate; and determining, by the computer-based system, loan termsthat are above the risk free rate, wherein said loan terms are based atleast in part upon the weighted expected probability of default, acapital structure of a lender, and finding rates available to thelender.